In [1]:
import sys
import os
sys.path.insert(0, os.path.abspath('../'))
sys.path.insert(0, os.path.abspath('../../'))
sys.path.insert(0, os.path.abspath('/home/hm-tlacherm/qlm_notebooks/notebooks_1.2.1/notebooks/master_thesis_qaoa/'))
sys.path.insert(0, os.path.abspath('/home/hm-tlacherm/qlm_notebooks/notebooks_1.2.1/notebooks/master_thesis_qaoa/ibm/'))
In [2]:
import numpy as np

import qiskit
provider = qiskit.IBMQ.load_account()
from qiskit import Aer
from qiskit.utils import QuantumInstance
from qiskit_optimization.algorithms import MinimumEigenOptimizer
from qiskit.algorithms import QAOA
from shared.QiskitMaxcut import *
from ibm.ibm_parameters import *

from matplotlib import pyplot as plt
%matplotlib inline

from ibm_landscape_processes import *

ibmqfactory.load_account:WARNING:2021-08-23 22:16:08,897: Credentials are already in use. The existing account in the session will be replaced.
In [3]:
%load_ext autoreload
%autoreload 2
In [4]:
# ---- Define graph and MaxCut ----
graph = load_nx_graph_from("/home/hm-tlacherm/qlm_notebooks/notebooks_1.2.1/notebooks/master_thesis_qaoa/data/graphs/10_nodes/graph_10_29_01.txt")
max_cut = Maxcut(graph)
max_cut_qubo = max_cut.to_qubo()
max_cut.draw()
In [5]:
# ---- Define step size and gamma, beta values ----
step_size = 0.1
a_gamma = np.arange(-np.pi, np.pi, step_size)
b_beta = np.arange(-np.pi, np.pi, step_size)
In [6]:
gammas, betas = np.meshgrid(a_gamma, b_beta)
In [7]:
# ---- execute for all values QAOA and get result matrix  ----
landscape = run_all(gammas, betas, max_cut)

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In [8]:
print(landscape)
plt.matshow(landscape)
plt.show()

[[-14.50837517 -14.45562458 -14.51099968 ... -14.40687466 -14.51099968
  -14.5638752 ]
 [-14.50650024 -14.15674973 -13.85400009 ... -15.21562481 -15.00625038
  -14.73512459]
 [-14.50362492 -13.87425041 -13.29125023 ... -15.7371254  -15.45337486
  -14.99037457]
 ...
 [-14.48087502 -15.23187542 -15.81212521 ... -12.51350021 -13.12012482
  -13.87937546]
 [-14.50012493 -15.03999996 -15.47525024 ... -13.06375027 -13.48725033
  -13.96512508]
 [-14.56212521 -14.75212479 -14.94287491 ... -13.88475037 -14.01087475
  -14.27474976]]
In [9]:
# Mean of landscape
np.mean(landscape)
Out[9]:
-14.045207424408904
In [10]:
# Minimium 
np.min(landscape)
Out[10]:
-16.954500198364258
In [11]:
# Display Coordinates of Minimum 
np.unravel_index(np.argmin(landscape), landscape.shape)
Out[11]:
(35, 13)
In [12]:
# Gamma and beta value of Minimium
i, j = np.unravel_index(np.argmin(landscape), landscape.shape)
opt_gamma = gammas[i,j]
opt_beta = betas[i,j]
print(f"Opt.Gamma: {opt_gamma}, Opt.Beta: {opt_beta}")

Opt.Gamma: -1.841592653589792, Opt.Beta: 0.35840734641021
In [13]:
# Save result matrix 
with open('landscape_simulator_10_nodes_no_weights_results.npy', 'wb') as f:
    np.save(f, landscape)
In [14]:
import plotly.graph_objects as go
In [15]:
# Plot landscape in 3D 
a_gamma = np.arange(-np.pi, np.pi, step_size)
b_beta = np.arange(-np.pi, np.pi, step_size)
fig = go.Figure(data=go.Surface(z=landscape, x=a_gamma, y=b_beta))

fig.update_traces(contours_z=dict(show=True, usecolormap=True, highlightcolor='limegreen', project_z=True))


fig.update_layout(title="QAOA MaxCut", scene=dict(
    xaxis_title="gamma",
    yaxis_title="beta",
    zaxis_title="mean"
))
In [16]:
# Plot Heatmap 
fig = go.Figure(data=go.Heatmap(z=landscape, y=b_beta, x=a_gamma, type = 'heatmap', colorscale = 'viridis'))

# Update Layout
fig.update_layout(title="QAOA MaxCut", width=700, height=700, xaxis_title="beta", yaxis_title="gamma")

# Display Global Minimium 
fig.add_trace(
    go.Scatter(mode="markers", y=[opt_beta], x=[opt_gamma], marker_symbol=[204], text = [landscape[i,j]],
                   marker_color="red",  hovertemplate="x: %{x}<br>y: %{y}<br> z: %{text:.2f}<extra></extra>", 
                   marker_line_width=1, marker_size=16))
In [17]:
# Display Optimizer Results

# Display path 
#fig.add_trace(
#    go.Scatter(mode="lines", x=gammas, y=betas, marker_symbol=[200],
#                   marker_color="white", marker_line_width=1, marker_size=8)
#)

# Display start point
#fig.add_trace(
#    go.Scatter(mode="markers", x=[gammas[0]], y=[betas[0]], marker_symbol=[204],
#                   marker_color="gray", 
#                   marker_line_width=1, marker_size=16))

# Display end point
#fig.add_trace(
#    go.Scatter(mode="markers", x=[gammas[-1]], y=[betas[-1]], marker_symbol=[204],
#                   marker_color="green", 
#                   marker_line_width=1, marker_size=16))
In [18]:
# Plot Optimizer History
#fig = go.Figure(data=go.Scatter(x=counts, y=values))
#fig.update_layout(xaxis_title="Evaluation Counts", yaxis_title="Evaluated Mean", title="Optimizer")
#fig.show()